官网实例详解4.13（imdb_bidirectional_lstm.py）-keras学习笔记四

Keras实例目录

代码注释

'''Trains a Bidirectional LSTM on the IMDB sentiment classification task.
为IMDB情感分类任务训练双向LSTM网络
Output after 4 epochs on CPU: ~0.8146
基于CPU（运行）4个周期后输出: ~0.8146
Time per epoch on CPU (Core i7): ~150s.
基于CPU(Core i7)（运行）每个周期用时：~150s.
'''

from __future__ import print_function
import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.datasets import imdb


max_features = 20000
# cut texts after this number of words
# (among top max_features most common words)
# 在这个（单词的）数量之后剪切文本（在max_features最常用的单词中）
maxlen = 100
batch_size = 32

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
y_train = np.array(y_train)
y_test = np.array(y_test)

model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# try using different optimizers and different optimizer configs
# 尝试使用不同的优化器和不同的优化器配置
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=4,
          validation_data=[x_test, y_test])

代码执行

 

Keras详细介绍

英文：https://keras.io/

中文：http://keras-cn.readthedocs.io/en/latest/

实例下载

https://github.com/keras-team/keras

https://github.com/keras-team/keras/tree/master/examples

完整项目下载

方便没积分童鞋，请加企鹅452205574，共享文件夹。

包括：代码、数据集合（图片）、已生成model、安装库文件等。